Digital Forming

Digital data processing is widely used in industrial manufacturing. Planning, design, control and regulation of forming processes also become more and more digital.

The research topics embedded in this cross-sectional area are assigned to different research groups and are presented below.

Roller Leveling

Research in field of roller leveling aims on developing a process control for an automatic setting of the leveler according to the characteristics of the strip to be leveled. The process control shall identify variations in the strip characteristics, e.g. material properties, and compensate them. Therefore, the force in the first load triangle is measured. This measurement is used as a reference value to correlate strip characteristics and the optimum setting of the leveler. In order to generate the necessary data, a wide variety of parameters is calculated within an FE model of the leveling process. Based on the results the control concept is transferred to an actual leveling machine. According to the results the process control is able to detect changes of the strip characteristics and to compensate those successfully. In addition to the flatness of the strip the possibility to set defined residual stress distributions within the strip is investigated.

In a joined project with the Institute of Automatic Control of the RWTH Aachen University financed by the Deutsche Forschungsgesellschaft, new approaches for further enhancing the thickness tolerances of cold rolled metal strips are investigated. Particularly in new rolling mills with high rolling speeds, the dynamics of electromechanical or hydraulic actuators for roll gap adjustment are too slow to compensate high-frequency interferences in the strip. In comparison to electromechanical or hydraulic actuators, piezoelectric actuators show significantly better dynamics. In order to investigate the suitability of such actuators for a high dynamic roll gap adjustment, two piezoelectric stack actuators are installed in a test rolling mill of industrial scale. The piezoelectric actuators are embedded into a model predictive controller, which finds the optimal, high-dynamic trajectory of the actuators stroke.

Fast process models enable the accurate simulation of heavy plate rolling on the industrial and laboratory scale. Based on the pass schedule and material parameters it predicts the most important properties, such as force, temperature and microstructure, within seconds. Thus it has a wide range of applications, particularly in the field of design and optimization. With Industry 4.0 in mind, it has been coupled to a data base of industrial trials resulting in the ability to determine material parameters from just the measured forces. It has furthermore been coupled with machine learning algorithms to automatically design pass schedules. Fast process models are also being used for teaching and seminars, supplemented by a specially created graphical user interface. It allows students and seminar participants to develop an intuitive approach to the design, calculation and optimization of pass schedule as well as a detailed understanding of the underlying mechanisms.

The design of pass schedules for rolling is based on expert and empirical knowledge. This is due to each pass influencing all subsequent passes. Machine learning algorithms could provide an approach to automatize the design of pass schedules. By training them they can derive knowledge from data without needing an explicit mathematical formulation. As a proof of concept a data base has been generated using a fast process model. A neural network has then been trained to design pass schedules based on the data provided, if the initial and final state of the workpiece are provided as input. The boundary conditions are given by the rolling mill available at the IBF. The designed pass schedule fulfills all boundary conditions while exactly meeting the final state of the workpiece. Therefor the automatized design of pass schedules using machine learning algorithms seems feasible.

Open-die forging is an incremental forming process, where the initial ingot is forged in up to many hundred forming steps towards the final geometry. The design of new forging process is mainly realized based upon experience or simple geometric correlations. However, by this only a simple geometrical-based process design is possible which does not give any statement about the temperature, the equivalent strain and the grain size. Since FE-simulation of open-die forging is very time consuming and requires a large numerical effort, the IBF developed fast calculation models for open-die forging, which allow the fast calculation of these decisive target values within seconds. Combined with a GUI, a property based design and optimization of open-die forging process can be successfully realized. Furthermore, these models offer a significant potential for teaching purposes as the correlations between forging parameters and resulting workpiece properties can be analyzed in a descriptive way.

To ensure excellent and homogeneous mechanical properties, industrial open-die forging requires a smooth and controlled forging sequence. However, during the actual forging process it is not possible to measure decisive workpiece properties as the grain size, equivalent strain and the temperature distribution within the workpiece. Therefore, it is not possible for the press operator to evaluate the impact of small deviations from the planned process sequence on the resulting workpiece properties. Thus, the following process sequence cannot be optimized with respect e.g. to the microstructure. Due to this issue, one main research topic in the field of forging at IBF is the development of an assisting system for open-die forging. By applying fast process models for temperature, strain and grain size, the system allows the operator to evaluate the workpiece properties based upon measured and calculated values. This approach follows the vision to develop an autonomous process control for open-die forging.

Minimizing material and milling costs by near-net shape ring rolling today plays an important economical and environmental role. Industrially feasible ring geometries are exclusively rotationally symmetric, even though applications with varying volume distribution around the circumference exist, e.g. eccentric rings with a nearly linear wall thickness distribution. Depending on size, these parts are produced by milling, closed-die forging or casting processes, while disadvantages in terms of material waste, process flexibility or mechanical properties have to be accepted.
This project aims to further develop the ring rolling process to enable production of near-net shape eccentric ring geometries. Especially for large parts this allows for large material savings without restricting process flexibility or product properties.

In prototyping and small batch production, conventional manufacturing processes, such as deep drawing, are usually not economically applicable. Flexible processes with low tooling effort, such as stretch forming and Incremental Sheet Forming, short ISF, are a promising alternative to realize parts within a very short time. In addition, the goal "first time right" is pursued to save resources. Therefore, reliable and precise planning tools and models are needed. The integrated CAx process chain, developed at the IBF, enables the process planning in a CAD-CAM environment with corresponding interfaces to FE models and digital image correlation tools. Numerical simulations of stretch forming or ISF provide digital geometries that can be compared with the target geometry. In so doing, iteration cycles during the prototype production are performed virtually while material-intensive and time-consuming experiments are avoided.

Other Resources

Institutions

The content embedded in this web page will take you to web pages provided by the Google-operated video sharing platform YouTube – YouTube, LLC, 901 Cherry Ave., San Bruno, CA 94066, USA. Invoking this content makes it possible for YouTube to determine your IP address, the language setting of your system, and a number of browser-specific details.

If you are logged in to your YouTube account, you make it possible for YouTube to tie your web browsing behavior directly to your personal profile. You can prevent this by logging out of your YouTube account.

YouTube uses cookies and tracking tools. Information on YouTube's data processing activities and the purpose of these activities is available and can be viewed at YouTube.

By clicking the "accept" button, you agree to use the content provided by the platform under the conditions outlined above.

Please note that you give your consent for a one-time use of the web page only. When you visit the page again, you will again be asked for your consent.

The content embedded in this web page will take you to web pages provided by the Vimeo – Vimeo LLC, 555 West 18th Street, New York, New York 10011. Invoking this content makes it possible for Vimeo to determine your IP address, the language setting of your system, and a number of browser-specific details.

If you are logged in to your Vimeo account, you make it possible for Vimeo to tie your web browsing behavior directly to your personal profile. You can prevent this by logging out of your Vimeo account.

Vimeo uses cookies and tracking tools. Information on YouTube's data processing activities and the purpose of these activities is available and can be viewed at Vimeo.

By clicking the "accept" button, you agree to use the content provided by the platform under the conditions outlined above.

Please note that you give your consent for a one-time use of the web page only. When you visit the page again, you will again be asked for your consent.